import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


lowerCamelCase__  =					{
    '''distilbert''': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    '''roberta''': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    '''bert''': (BertConfig, BertForMaskedLM, BertTokenizer),
    '''gpt2''': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}


def 				lowerCAmelCase__       (			a__ )      ->Optional[Any]:




  '''simple docstring'''

  assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
  assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
  if args.mlm:
    assert os.path.isfile(args.token_counts )
    assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
  else:
    assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

  assert args.teacher_type == args.student_type or (
      args.student_type == "distilbert" and args.teacher_type == "bert"
  )
  assert os.path.isfile(args.student_config )
  if args.student_pretrained_weights is not None:
    assert os.path.isfile(args.student_pretrained_weights )

  if args.freeze_token_type_embds:
    assert args.student_type in ["roberta"]

  assert args.alpha_ce >= 0.0
  assert args.alpha_mlm >= 0.0
  assert args.alpha_clm >= 0.0
  assert args.alpha_mse >= 0.0
  assert args.alpha_cos >= 0.0
  assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0


def 				lowerCAmelCase__       (			a__    ,     a__ )      ->List[Any]:




  '''simple docstring'''

  if args.student_type == "roberta":
    _UpperCamelCase           =    False
  elif args.student_type == "gpt2":
    _UpperCamelCase           =    False


def 				lowerCAmelCase__       (			a__    ,     a__ )      ->List[str]:




  '''simple docstring'''

  if args.student_type == "roberta":
    _UpperCamelCase           =    False


def 				lowerCAmelCase__       (			)      ->Any:




  '''simple docstring'''

  _UpperCamelCase           =    argparse.ArgumentParser(description="Training" )
  parser.add_argument("--force"    ,     action="store_true"    ,     help="Overwrite dump_path if it already exists." )

  parser.add_argument(
      "--dump_path"    ,     type=a__    ,     required=a__    ,     help="The output directory (log, checkpoints, parameters, etc.)" )
  parser.add_argument(
      "--data_file"    ,     type=a__    ,     required=a__    ,     help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence."    ,     )

  parser.add_argument(
      "--student_type"    ,     type=a__    ,     choices=["distilbert", "roberta", "gpt2"]    ,     required=a__    ,     help="The student type (DistilBERT, RoBERTa)."    ,     )
  parser.add_argument("--student_config"    ,     type=a__    ,     required=a__    ,     help="Path to the student configuration." )
  parser.add_argument(
      "--student_pretrained_weights"    ,     default=a__    ,     type=a__    ,     help="Load student initialization checkpoint." )

  parser.add_argument(
      "--teacher_type"    ,     choices=["bert", "roberta", "gpt2"]    ,     required=a__    ,     help="Teacher type (BERT, RoBERTa)." )
  parser.add_argument("--teacher_name"    ,     type=a__    ,     required=a__    ,     help="The teacher model." )

  parser.add_argument("--temperature"    ,     default=2.0    ,     type=a__    ,     help="Temperature for the softmax temperature." )
  parser.add_argument(
      "--alpha_ce"    ,     default=0.5    ,     type=a__    ,     help="Linear weight for the distillation loss. Must be >=0." )
  parser.add_argument(
      "--alpha_mlm"    ,     default=0.0    ,     type=a__    ,     help="Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag."    ,     )
  parser.add_argument("--alpha_clm"    ,     default=0.5    ,     type=a__    ,     help="Linear weight for the CLM loss. Must be >=0." )
  parser.add_argument("--alpha_mse"    ,     default=0.0    ,     type=a__    ,     help="Linear weight of the MSE loss. Must be >=0." )
  parser.add_argument(
      "--alpha_cos"    ,     default=0.0    ,     type=a__    ,     help="Linear weight of the cosine embedding loss. Must be >=0." )

  parser.add_argument(
      "--mlm"    ,     action="store_true"    ,     help="The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM." )
  parser.add_argument(
      "--mlm_mask_prop"    ,     default=0.15    ,     type=a__    ,     help="Proportion of tokens for which we need to make a prediction."    ,     )
  parser.add_argument("--word_mask"    ,     default=0.8    ,     type=a__    ,     help="Proportion of tokens to mask out." )
  parser.add_argument("--word_keep"    ,     default=0.1    ,     type=a__    ,     help="Proportion of tokens to keep." )
  parser.add_argument("--word_rand"    ,     default=0.1    ,     type=a__    ,     help="Proportion of tokens to randomly replace." )
  parser.add_argument(
      "--mlm_smoothing"    ,     default=0.7    ,     type=a__    ,     help="Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec)."    ,     )
  parser.add_argument("--token_counts"    ,     type=a__    ,     help="The token counts in the data_file for MLM." )

  parser.add_argument(
      "--restrict_ce_to_mask"    ,     action="store_true"    ,     help="If true, compute the distillation loss only the [MLM] prediction distribution."    ,     )
  parser.add_argument(
      "--freeze_pos_embs"    ,     action="store_true"    ,     help="Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only."    ,     )
  parser.add_argument(
      "--freeze_token_type_embds"    ,     action="store_true"    ,     help="Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only."    ,     )

  parser.add_argument("--n_epoch"    ,     type=a__    ,     default=3    ,     help="Number of pass on the whole dataset." )
  parser.add_argument("--batch_size"    ,     type=a__    ,     default=5    ,     help="Batch size (for each process)." )
  parser.add_argument(
      "--group_by_size"    ,     action="store_false"    ,     help="If true, group sequences that have similar length into the same batch. Default is true."    ,     )

  parser.add_argument(
      "--gradient_accumulation_steps"    ,     type=a__    ,     default=50    ,     help="Gradient accumulation for larger training batches."    ,     )
  parser.add_argument("--warmup_prop"    ,     default=0.05    ,     type=a__    ,     help="Linear warmup proportion." )
  parser.add_argument("--weight_decay"    ,     default=0.0    ,     type=a__    ,     help="Weight decay if we apply some." )
  parser.add_argument("--learning_rate"    ,     default=5e-4    ,     type=a__    ,     help="The initial learning rate for Adam." )
  parser.add_argument("--adam_epsilon"    ,     default=1e-6    ,     type=a__    ,     help="Epsilon for Adam optimizer." )
  parser.add_argument("--max_grad_norm"    ,     default=5.0    ,     type=a__    ,     help="Max gradient norm." )
  parser.add_argument("--initializer_range"    ,     default=0.02    ,     type=a__    ,     help="Random initialization range." )

  parser.add_argument(
      "--fp16"    ,     action="store_true"    ,     help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"    ,     )
  parser.add_argument(
      "--fp16_opt_level"    ,     type=a__    ,     default="O1"    ,     help=(
          "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
          "See details at https://nvidia.github.io/apex/amp.html"
      )    ,     )
  parser.add_argument("--n_gpu"    ,     type=a__    ,     default=1    ,     help="Number of GPUs in the node." )
  parser.add_argument("--local_rank"    ,     type=a__    ,     default=-1    ,     help="Distributed training - Local rank" )
  parser.add_argument("--seed"    ,     type=a__    ,     default=56    ,     help="Random seed" )

  parser.add_argument("--log_interval"    ,     type=a__    ,     default=500    ,     help="Tensorboard logging interval." )
  parser.add_argument("--checkpoint_interval"    ,     type=a__    ,     default=4_000    ,     help="Checkpoint interval." )
  _UpperCamelCase           =    parser.parse_args()
  sanity_checks(a__ )

  # ARGS #
  init_gpu_params(a__ )
  set_seed(a__ )
  if args.is_master:
    if os.path.exists(args.dump_path ):
      if not args.force:
        raise ValueError(
            f'Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'
            " itUse `--force` if you want to overwrite it" )
      else:
        shutil.rmtree(args.dump_path )

    if not os.path.exists(args.dump_path ):
      os.makedirs(args.dump_path )
    logger.info(f'Experiment will be dumped and logged in {args.dump_path}' )

    # SAVE PARAMS #
    logger.info(f'Param: {args}' )
    with open(os.path.join(args.dump_path    ,     "parameters.json" )    ,     "w" ) as f:
      json.dump(vars(a__ )    ,     a__    ,     indent=4 )
    git_log(args.dump_path )

  _UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase           =    MODEL_CLASSES[args.student_type]
  _UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase           =    MODEL_CLASSES[args.teacher_type]

  # TOKENIZER #
  _UpperCamelCase           =    teacher_tokenizer_class.from_pretrained(args.teacher_name )
  _UpperCamelCase           =    {}
  for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
    _UpperCamelCase           =    tokenizer.all_special_tokens.index(a__ )
    _UpperCamelCase           =    tokenizer.all_special_ids[idx]
  logger.info(f'Special tokens {special_tok_ids}' )
  _UpperCamelCase           =    special_tok_ids
  _UpperCamelCase           =    tokenizer.max_model_input_sizes[args.teacher_name]

  # DATA LOADER #
  logger.info(f'Loading data from {args.data_file}' )
  with open(args.data_file    ,     "rb" ) as fp:
    _UpperCamelCase           =    pickle.load(a__ )

  if args.mlm:
    logger.info(f'Loading token counts from {args.token_counts} (already pre-computed)' )
    with open(args.token_counts    ,     "rb" ) as fp:
      _UpperCamelCase           =    pickle.load(a__ )

    _UpperCamelCase           =    np.maximum(a__    ,     1 ) ** -args.mlm_smoothing
    for idx in special_tok_ids.values():
      _UpperCamelCase           =    0.0  # do not predict special tokens
    _UpperCamelCase           =    torch.from_numpy(a__ )
  else:
    _UpperCamelCase           =    None

  _UpperCamelCase           =    LmSeqsDataset(params=a__    ,     data=a__ )
  logger.info("Data loader created." )

  # STUDENT #
  logger.info(f'Loading student config from {args.student_config}' )
  _UpperCamelCase           =    student_config_class.from_pretrained(args.student_config )
  _UpperCamelCase           =    True

  if args.student_pretrained_weights is not None:
    logger.info(f'Loading pretrained weights from {args.student_pretrained_weights}' )
    _UpperCamelCase           =    student_model_class.from_pretrained(args.student_pretrained_weights    ,     config=a__ )
  else:
    _UpperCamelCase           =    student_model_class(a__ )

  if args.n_gpu > 0:
    student.to(f'cuda:{args.local_rank}' )
  logger.info("Student loaded." )

  # TEACHER #
  _UpperCamelCase           =    teacher_model_class.from_pretrained(args.teacher_name    ,     output_hidden_states=a__ )
  if args.n_gpu > 0:
    teacher.to(f'cuda:{args.local_rank}' )
  logger.info(f'Teacher loaded from {args.teacher_name}.' )

  # FREEZING #
  if args.freeze_pos_embs:
    freeze_pos_embeddings(a__    ,     a__ )
  if args.freeze_token_type_embds:
    freeze_token_type_embeddings(a__    ,     a__ )

  # SANITY CHECKS #
  assert student.config.vocab_size == teacher.config.vocab_size
  assert student.config.hidden_size == teacher.config.hidden_size
  assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
  if args.mlm:
    assert token_probs.size(0 ) == stu_architecture_config.vocab_size

  # DISTILLER #
  torch.cuda.empty_cache()
  _UpperCamelCase           =    Distiller(
      params=a__    ,     dataset=a__    ,     token_probs=a__    ,     student=a__    ,     teacher=a__ )
  distiller.train()
  logger.info("Let's go get some drinks." )


if __name__ == "__main__":
   main()





import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
   from transformers import SpeechaTextFeatureExtractor

lowerCamelCase__  =					random.Random()


def 				lowerCAmelCase__       (			a__    ,     a__=1.0    ,     a__=None    ,     a__=None )      ->Optional[Any]:




  '''simple docstring'''

  if rng is None:
    _UpperCamelCase           =    global_rng

  _UpperCamelCase           =    []
  for batch_idx in range(shape[0] ):
    values.append([] )
    for _ in range(shape[1] ):
      values[-1].append(rng.random() * scale )

  return values




@require_torch
@require_torchaudio
class 					_UpperCAmelCase			(     unittest.TestCase  ):



 '''simple docstring'''




 def __init__(		self				:   Optional[Any]							, lowercase_				:   List[str]							, lowercase_				:   List[str]=7							, lowercase_				:   List[str]=400							, lowercase_				:   str=2000							, lowercase_				:   Optional[int]=24							, lowercase_				:   Optional[int]=24							, lowercase_				:   Tuple=0.0							, lowercase_				:   Any=16000							, lowercase_				:   Any=True							, lowercase_				:   Optional[int]=True							, )						->	Union[str, Any]:


   """simple docstring"""







   _UpperCamelCase           =    parent
   _UpperCamelCase           =    batch_size
   _UpperCamelCase           =    min_seq_length
   _UpperCamelCase           =    max_seq_length
   _UpperCamelCase           =    (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
   _UpperCamelCase           =    feature_size
   _UpperCamelCase           =    num_mel_bins
   _UpperCamelCase           =    padding_value
   _UpperCamelCase           =    sampling_rate
   _UpperCamelCase           =    return_attention_mask
   _UpperCamelCase           =    do_normalize







 def 		__UpperCAmelCase				(		self				:   int)						->	str:


   """simple docstring"""







   return {
       "feature_size": self.feature_size,
       "num_mel_bins": self.num_mel_bins,
       "padding_value": self.padding_value,
       "sampling_rate": self.sampling_rate,
       "return_attention_mask": self.return_attention_mask,
       "do_normalize": self.do_normalize,
   }







 def 		__UpperCAmelCase				(		self				:   Tuple							, lowercase_				:   Optional[Any]=False							, lowercase_				:   int=False)						->	Dict:


   """simple docstring"""







   def _flatten(lowercase_				:   str):
     return list(itertools.chain(*lowercase_))

   if equal_length:
     _UpperCamelCase           =    [floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)]
   else:
     # make sure that inputs increase in size
     _UpperCamelCase           =    [
         floats_list((x, self.feature_size))
         for x in range(self.min_seq_length							, self.max_seq_length							, self.seq_length_diff)
     ]
   if numpify:
     _UpperCamelCase           =    [np.asarray(lowercase_) for x in speech_inputs]
   return speech_inputs




@require_torch
@require_torchaudio
class 					_UpperCAmelCase			(     lowerCAmelCase,			unittest.TestCase  ):



 '''simple docstring'''




 __A			       =					SpeechaTextFeatureExtractor if is_speech_available() else None







 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	List[Any]:


   """simple docstring"""







   _UpperCamelCase           =    SpeechaTextFeatureExtractionTester(self)







 def 		__UpperCAmelCase				(		self				:   Dict							, lowercase_				:   Tuple)						->	int:


   """simple docstring"""







   self.assertTrue(np.all(np.mean(lowercase_							, axis=0) < 1e-3))
   self.assertTrue(np.all(np.abs(np.var(lowercase_							, axis=0) - 1) < 1e-3))







 def 		__UpperCAmelCase				(		self				:   List[Any])						->	int:


   """simple docstring"""







   _UpperCamelCase           =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
   # create three inputs of length 800, 1000, and 1200
   _UpperCamelCase           =    [floats_list((1, x))[0] for x in range(800							, 1400							, 200)]
   _UpperCamelCase           =    [np.asarray(lowercase_) for speech_input in speech_inputs]

   # Test feature size
   _UpperCamelCase           =    feature_extractor(lowercase_							, padding=lowercase_							, return_tensors="np").input_features
   self.assertTrue(input_features.ndim == 3)
   self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size)

   # Test not batched input
   _UpperCamelCase           =    feature_extractor(speech_inputs[0]							, return_tensors="np").input_features
   _UpperCamelCase           =    feature_extractor(np_speech_inputs[0]							, return_tensors="np").input_features
   self.assertTrue(np.allclose(lowercase_							, lowercase_							, atol=1e-3))

   # Test batched
   _UpperCamelCase           =    feature_extractor(lowercase_							, return_tensors="np").input_features
   _UpperCamelCase           =    feature_extractor(lowercase_							, return_tensors="np").input_features
   for enc_seq_a, enc_seq_a in zip(lowercase_							, lowercase_):
     self.assertTrue(np.allclose(lowercase_							, lowercase_							, atol=1e-3))

   # Test 2-D numpy arrays are batched.
   _UpperCamelCase           =    [floats_list((1, x))[0] for x in (800, 800, 800)]
   _UpperCamelCase           =    np.asarray(lowercase_)
   _UpperCamelCase           =    feature_extractor(lowercase_							, return_tensors="np").input_features
   _UpperCamelCase           =    feature_extractor(lowercase_							, return_tensors="np").input_features
   for enc_seq_a, enc_seq_a in zip(lowercase_							, lowercase_):
     self.assertTrue(np.allclose(lowercase_							, lowercase_							, atol=1e-3))







 def 		__UpperCAmelCase				(		self				:   Dict)						->	List[Any]:


   """simple docstring"""







   _UpperCamelCase           =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
   _UpperCamelCase           =    [floats_list((1, x))[0] for x in range(800							, 1400							, 200)]

   _UpperCamelCase           =    ["longest", "max_length", "do_not_pad"]
   _UpperCamelCase           =    [None, 16, None]
   for max_length, padding in zip(lowercase_							, lowercase_):
     _UpperCamelCase           =    feature_extractor(
         lowercase_							, padding=lowercase_							, max_length=lowercase_							, return_attention_mask=lowercase_)
     _UpperCamelCase           =    inputs.input_features
     _UpperCamelCase           =    inputs.attention_mask
     _UpperCamelCase           =    [np.sum(lowercase_) for x in attention_mask]

     self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]])
     self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]])
     self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]])







 def 		__UpperCAmelCase				(		self				:   int)						->	str:


   """simple docstring"""







   _UpperCamelCase           =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
   _UpperCamelCase           =    [floats_list((1, x))[0] for x in range(800							, 1400							, 200)]

   _UpperCamelCase           =    ["longest", "max_length", "do_not_pad"]
   _UpperCamelCase           =    [None, 16, None]
   for max_length, padding in zip(lowercase_							, lowercase_):
     _UpperCamelCase           =    feature_extractor(
         lowercase_							, max_length=lowercase_							, padding=lowercase_							, return_tensors="np"							, return_attention_mask=lowercase_)
     _UpperCamelCase           =    inputs.input_features
     _UpperCamelCase           =    inputs.attention_mask
     _UpperCamelCase           =    [np.sum(lowercase_) for x in attention_mask]

     self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]])
     self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6)
     self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]])
     self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6)
     self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]])







 def 		__UpperCAmelCase				(		self				:   int)						->	Optional[int]:


   """simple docstring"""







   _UpperCamelCase           =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
   _UpperCamelCase           =    [floats_list((1, x))[0] for x in range(800							, 1400							, 200)]
   _UpperCamelCase           =    feature_extractor(
       lowercase_							, padding="max_length"							, max_length=4							, truncation=lowercase_							, return_tensors="np"							, return_attention_mask=lowercase_							, )
   _UpperCamelCase           =    inputs.input_features
   _UpperCamelCase           =    inputs.attention_mask
   _UpperCamelCase           =    np.sum(attention_mask == 1							, axis=1)

   self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]])
   self._check_zero_mean_unit_variance(input_features[1])
   self._check_zero_mean_unit_variance(input_features[2])







 def 		__UpperCAmelCase				(		self				:   Dict)						->	Optional[int]:


   """simple docstring"""







   _UpperCamelCase           =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
   _UpperCamelCase           =    [floats_list((1, x))[0] for x in range(800							, 1400							, 200)]
   _UpperCamelCase           =    feature_extractor(
       lowercase_							, padding="longest"							, max_length=4							, truncation=lowercase_							, return_tensors="np"							, return_attention_mask=lowercase_							, )
   _UpperCamelCase           =    inputs.input_features
   _UpperCamelCase           =    inputs.attention_mask
   _UpperCamelCase           =    np.sum(attention_mask == 1							, axis=1)

   self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]])
   self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]])
   self._check_zero_mean_unit_variance(input_features[2])

   # make sure that if max_length < longest -> then pad to max_length
   self.assertEqual(input_features.shape							, (3, 4, 24))

   _UpperCamelCase           =    [floats_list((1, x))[0] for x in range(800							, 1400							, 200)]
   _UpperCamelCase           =    feature_extractor(
       lowercase_							, padding="longest"							, max_length=16							, truncation=lowercase_							, return_tensors="np"							, return_attention_mask=lowercase_							, )
   _UpperCamelCase           =    inputs.input_features
   _UpperCamelCase           =    inputs.attention_mask
   _UpperCamelCase           =    np.sum(attention_mask == 1							, axis=1)

   self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]])
   self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]])
   self._check_zero_mean_unit_variance(input_features[2])

   # make sure that if max_length < longest -> then pad to max_length
   self.assertEqual(input_features.shape							, (3, 6, 24))







 def 		__UpperCAmelCase				(		self				:   Tuple)						->	Dict:


   """simple docstring"""







   import torch

   _UpperCamelCase           =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
   _UpperCamelCase           =    np.random.rand(100							, 32).astype(np.floataa)
   _UpperCamelCase           =    np_speech_inputs.tolist()

   for inputs in [py_speech_inputs, np_speech_inputs]:
     _UpperCamelCase           =    feature_extractor.pad([{"input_features": inputs}]							, return_tensors="np")
     self.assertTrue(np_processed.input_features.dtype == np.floataa)
     _UpperCamelCase           =    feature_extractor.pad([{"input_features": inputs}]							, return_tensors="pt")
     self.assertTrue(pt_processed.input_features.dtype == torch.floataa)







 def 		__UpperCAmelCase				(		self				:   str							, lowercase_				:   List[str])						->	Dict:


   """simple docstring"""







   from datasets import load_dataset

   _UpperCamelCase           =    load_dataset("hf-internal-testing/librispeech_asr_dummy"							, "clean"							, split="validation")
   # automatic decoding with librispeech
   _UpperCamelCase           =    ds.sort("id").select(range(lowercase_))[:num_samples]["audio"]

   return [x["array"] for x in speech_samples]







 def 		__UpperCAmelCase				(		self				:   str)						->	Tuple:


   """simple docstring"""







   _UpperCamelCase           =    np.array([
       -1.57_45, -1.77_13, -1.70_20, -1.60_69, -1.22_50, -1.11_05, -0.90_72, -0.82_41,
       -1.23_10, -0.80_98, -0.33_20, -0.41_01, -0.79_85, -0.49_96, -0.82_13, -0.91_28,
       -1.04_20, -1.12_86, -1.04_40, -0.79_99, -0.84_05, -1.22_75, -1.54_43, -1.46_25,
   ])
   # fmt: on

   _UpperCamelCase           =    self._load_datasamples(1)
   _UpperCamelCase           =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
   _UpperCamelCase           =    feature_extractor(lowercase_							, return_tensors="pt").input_features
   self.assertEquals(input_features.shape							, (1, 584, 24))
   self.assertTrue(np.allclose(input_features[0, 0, :30]							, lowercase_							, atol=1e-4))




